In such a domain, classical techniques used in multilingual terminology cannot be used as the term cannot be defined by reference to a stable/shared semantic level (each country having its own set of non-equivalent legal concepts).

establishing these indirect relations is not one of the main objectives of the LexALP project and would require more direct contrastive analysis.


The overall access value of the thesaurus rises very quickly after initial translations.

we fully expect these results to hold across larger samples and for more sophisticated translation systems.


Collocational information is not only useful, but also indispensable in many applications.

other definitions state explicitly that a collocation is an expression of language: "co-occurrence of two or more lexical items as realizations of structural elements within a given syntactic pattern" (Cowie, 1978); "a sequence of two or more consecutive words, that has characteristics of a syntactic and semantic unit" (Choueka, 1988).


Table 4 shows the comparative evaluation results for precision at different levels in the list of best-scored pairs, both with respect to grammaticality and to collocability (or, more exactly, the potential of a pair to constitute a MWE).

from the 500 initial pairs retrieved with each method, 497 pairs were retained in the first case (parse method), and 483 pairs in the second (window method).


Here, the forest covers all minimal derivations that correspond to G. It is necessary to ensure that for each derivation, each unaligned item (here ) appears only once in the rules of that derivation, as shown in Figure 2 (which satisfies the property).

in the minimal-rule extraction of GHKM, only three rules are extracted from the example corpus, i.e.


Token-level independence assumptions based on word-alignments can be used to decompose parallel corpora into manageable units for parameter estimation.

aligning tokens in parallel sentences using the IBM Models (Brown et al., 1993), (Och and Ney, 2003) may require less information than full-blown translation since the task is constrained by the source and target tokens present in each sentence pair.


To induce models of translational equivalence, some researchers have tried to use such trees to constrain bilingual constituents: The span of every node in the constraining parse tree must coincide with the relevant monolingual span of some node in the bilingual derivation tree.

if a constraining tree is applied to the other sentence as shown in Figure 2b, then the word alignment and tree constraint conflict.


We see that most of the differences between the methods appear as differences among rare words, with the contribution of more common words being negligible.

the contributions of this paper are threefold: in proposing a langauge model with excellent performance and the accompanying advantages of Bayesian probabilistic models, in proposing a novel and efficient inference scheme for the model, and in establishing the direct correspondence between interpolated Kneser-Ney and the Bayesian approach.


Observation on NIL corpus discloses the anomalous and dynamic features of chat language.

there is a social urgency to understand online chat language text.


Besides chat terms holding same form as showed in Example-2, we find that emoticon is another major exception type.

99.2 percent of chat terms can be represented by phonetic mappings.


Thus, the misclassification function in equation 8does not represent the likelihood that S will be incorrectly segmented.

but it is more suitable to adopt a stepby-step growing algorithm illustrated in Figure 4.


Compared to M1, M2 decreases 0.98% of F-score.

the last row of table 1 shows that Jaccard Coeff * is worse than other models when the number of snippets is larger than 100.


Resnik (1995) obtained information content from WordNet and achieved correlation coefficient 0.745.

nE j denotes the same person as those in c .


These expressions in MEDLINE were automatically annotated with event types.

the postfixes of query numbers denote whether ontological identifiers are used.


Predicate argument structures are annotated as attributes, "mod" and "argX," which point to the IDs of the argument phrases.

our motivation for employing semantic retrieval  [sentence] >> ([word arg1="$subject" arg2="$object" rel_type="activation"] & ([phrase id="$subject"] > [entity_name type="gene" gene_id="GHS019685"]) & ([phrase cat="np" id="$object"] > [entity_name type="gene" gene_id="GHS009426"])) Figure 7: Queries of the extended region algebra for Query 4-3 (upper: keyword search, lower: semantic search) was to provide a device for the accurate identification of relational concepts.


(2005)    Using hand-crafted grammar-based generation systems (Langkilde-Geary, 2002;Callaway, 2003), it is possible to achieve very high results.

the generation architecture presented here builds on the integrated parsing architecture resources of .


Similarly, we can distinguish between the cognitive loads of processing different forms of spatial relations.

if both are present we can interpret the reference based on relative proximity to the landmark the square.


In this paper, we present a computational approach to the generation of spatial locative expressions in such situated contexts.

all these algorithms assume the GRE component has access to a predefined scene model.


Perspectives, however, are not always manifested when two pieces of text together are put together.

the perspective from which each article is written is labeled as either Palestinian or Israeli by the editors.


we saw a number of subjective expressions in Section 2.


Considering only the 16-word subset with both S and O senses (according to Judge 1), κ is .75, and for the 16-word subset for which Judge 1 gave only S or only O senses, κ is .73.

work on automatic subjectivity analysis falls into three main areas.


Second, a number of methods for subjectivity or sentiment analysis start with a set of seed words and then search through WordNet to find other subjective words (Kamps and Marx, 2002;Yu and Hatzivassiloglou, 2003;Hu and Liu, 2004;Kim and Hovy, 2004;Esuli and Sebastiani, 2005).

interestingly, this aspect can drastically affect the algorithm accuracy.


Except in pure pattern-based approaches, e.g.

on the down side, four out of 108 previous first place answers were lost.


Biterm language models are similar to bigram language models except that the constraint of order in terms is relaxed.

in order to get comparable evaluation, we apply our approach to TREC 2003 definitional QA task.


Note that for the experiments reported here, we manually augmented the disjunctive LFs for the 104 sentences in our sample to make greater use of the periphrastic capabilities of the COMIC grammar; it remains for future work to augment the COMIC sentence planner produce these more richly disjunctive LFs automatically.

whether w ¡¨@s ij A >w ¡¨@s ik A as desired.


By comparison, NGRAM-1 and NGRAM-2 achieve less than 20% of the possible gain.

the approach we introduce here can be employed with any speech synthesizer in principle.


When an entity occurs several times, these models would force all occurrences to take the same value.

• For training our second-stage CRF, we need to get predictions on our train data as well as test data.


While this majority label would encourage the second CRF to switch the labels of all occurrences of the token sequence to Location, it would not strongly discourage the CRF from labeling these as Organization, since there would be several occurrences of token sequences in the training data labeled Organization, with the majority label of the token sequence being Location.

capturing label-consistency at the level of the whole test corpus is particularly helpful for token sequences that appear only once in their documents, but occur a few times over the corpus, since they do not have strong nonlocal information from within the document.


Unfortunately, in the RMN model, the dependencies must be defined in the model structure before doing any inference, and so the authors use heuristic part-of-speech patterns, and then add dependencies between these text spans using clique templates.

this framework is capable of learning interesting off-diagonal patterns as well.


The efforts are centered on establishing the phonetic relationship between transliteration pairs.

a method that explores non-aligned text was proposed by harvesting katakana-English pairs from query logs (Brill et al, 2001).


Non-Aspirated Consonants: In case of nonaspirated consonants, Shahmukhi has more consonants than Gurmukhi, which follows the one symbol for one sound principle.

usually, transliteration is referred to phonetic translation of a word of some specific type (proper nouns, technical terms, etc) across languages with different writing systems.


In others works, for instance (Silva et.

we propose an approach for MDC based only on cognate NE identification.


Furthermore, the accuracy of the proposed method 'NB+EM' exceeds that of the 'NB' by 11%.

meaningless (non-word) strings caused by mor- # sentences with a time-slot tag value phological analysis error are presented as the symbol "---".


From time-annotated text, companies can figure out when customers use their products.

these previous work are different from ours, because these work only dealt with newswire text including a lot of explicit temporal expressions.


We specifically adopted the combination of the Naïve Bayes classifier and the EM algorithm.

we treat only sentences expressing an event.


PerfMatch, on the other hand, cannot handle one-to-many relationships, acts "cautiously" and aligns the English NP to a dummy node, leading to a recall error.

the success of these methods is due partly to the availability of large amounts of training data annotated with rich linguistic information.


P (F = E) >> P (F = E), proper names with low counts then are encouraged to link to proper names during training; and consequently, conditional probability mass would be more focused on correct name translations.

word alignment models trained with different constraints are compared to show their effects on the resulting phrase translation tables and the final translation performance.


To investigate the differences, we measured the degree to which each set of alignments violated the supplied parse trees, by counting the frequency of interior nodes that are not null aligned.

formally, our model describes p(a, f|e, t), but otherwise borrows heavily from the HMM alignment model of Ney and Vogel (1996).


If the first choice in stage 1 is to STOP at the current leaf, then stage 2 and 3 are unnecessary.

a host of discriminative methods have been introduced (Taskar et al., 2005;Moore, 2005;ayan and Dorr, 2006).


In a phrase-based system, this link would block extraction of the phrases [axés sur la carrièr ⇒ career oriented] and [les emplois ⇒ the jobs] because the error overlaps with both.

stage 2 (MOVE(n, d)): Again, conditioning on the type of the parentn of the current node n, we choose a siblingn based on the signed distance d = φn(n) − φn(n), where φn(n) is the index of n in the child list ofn.


In total, it increases from 24.1 to 24.4 for the 100K filtered corpus, and from 24.5 to 24.8 for 150K, respectively.

this sum is then normalized by the total probability mass of the N -best list.


Note that the single improvements achieved here are slightly below the 95%-significance level.

in (Callison-Burch et al., 2004), a generative model for word alignment is trained using unsupervised learning on parallel text.


Here, the Chinese words " " are not translated by Hiero at all.

since among all the 2-word candidate translations, the translation "every month" has the highest translation probability as assigned by the WsD classifier, it is chosen as the best matching translation for the chunk.


In effect, through maximizing the likelihood of (3), we obtain the a priori probability estimates as a by-product.

their work only investigated the use of active learning to reduce the annotation effort necessary for WSD, but 55 did not deal with the porting of a WSD system to a different domain.


In particular, if for a particular language, we have that f 1 is true, then the fact that the implication holds means that f 2 must be true.

the πs are Bernoulli random variables, so they are given independent Beta priors.


Not all examples admit a simple explanation and are worthy of further thought.

we compare the following systems: FLAT (our proposed flat model), LINGHIER (our model using the phylogenetic hierarchy), DISTHIER (our model using the areal hierarchy) and RANDOM (a model that ranks implications-that meet the three qualifications from the previous section-randomly).


If the kernel-trick (Taylor and Cristianini, 2004) is applied to online margin-based learning, a subset of the observed examples, called the active set, needs to be stored.

one way to deal with this is to filter out low-confidence features, but it is difficult to decide which features are important in online learning.


The initiation vector Û ½ is initialized to ¼ and for each round the algorithm observes a training example Ü ´Ë µ and predicts its label Ý ¼ to be either •½ or ½.

the model cannot be used for other applications.


It is difficult for previous methods using statistical techniques to capture such an error.

second, the function words and time words that we use form a key word list.


For example, Ngram language model is considered to be effective in writing evaluation (Burstein et al., 1998;Corston-Oliver et al., 2001).


81.75% on the Japanese dataset, when we use all the features.

after the processing, each sentence together with its label becomes a database tuple.


2The second feature is computed by the ratio of the number of unknown collocations (neither correct LCs nor erroneous LCs) to the number of collocations in each sentence.

existing statistical approaches focus on some pre-defined errors and the reported results are not attractive.


The performance of the statistical language models is often evaluated by perplexity or cross-entropy.

on total over 200 hours of data from the database was used for acoustic model training, of which less than half was speech.


Let there be 317 bins corresponding to each consonant inventory L. A bin corresponding to an inventory L is packed with s L consonants chosen uniformly at random (without repetition) from the pool of 541 available consonants.

linguistic research has documented a wide range of regularities across the sound systems of the world's languages.


The results of the test are noted in Table 2.

this definition does not take into account the complexity that is involved in communicating the information about the inventory in terms of its constituent features.


For all languages, the MRR of the Winnow algorithm with the total feature set was higher than the baseline.

indeed, for some rarely spoken languages, it is practically impossible to find enough training data.


The method can be trained using a small amount of training data, and the performance decreases only by a small degree when it is trained with a language different from the test data.

gildea and Jurafsky (1996) counted the number of features whose values are different, and used them as a substitution cost.


This feature is relevant for predicting a genitive case in Russian and definiteness in Arabic.

verbal prefixes include conjunction and negation, and suffixes include object pronouns.


Such lexicons can be created manually, or automatically from data.

for each model and language, we perform feature selection in the following manner.


In the next section we present evaluation results for this default setting (which is applied when the user clicks the Semantic Filter button), but human translators have further options -to filter by tags of individual words, to use semantic classes from SL or TL terms, etc.

typically there are several hundred elements in P', of which only a few are really useful for translation.


However, the two approaches generate indirect translation equivalence hypotheses on the basis of radically different material: the GIZA dictionary uses evidence from parallel corpora of ex-isting human translations, while our system recombines translation candidates on the basis of their distributional similarity in monolingual comparable corpora.

our system returns both descriptors used in human translation as a pairlack essential (ranked 41 without filtering and 22 with the default semantic filter).


Since there are no substantial differences between the figures for the newspaper texts and for the interviews, we report the results jointly for 556 translation problems in both selections (lower rank figures Table 2 Ranking: frequency, similarity and filter It can be seen from the table that ranking by similarity yields almost a twofold improvement for the average rank figures compared to the baseline.

it learns a translation strategy which is based on the distributional similarity of words in a monolingual corpus, and applies this strategy to novel, previously unseen examples.


The small decline in ORD figures here can be attributed to the fact that there is a difference in genre between written and spoken texts and consequently between transformation types in them.

we computed recall on sets of human solutions rather than on individual solutions.


Extending word to phrase alignments attempts to address some of these situations while alleviating the noise in word-level alignments.

we had to limit to a permutation window size of 10 due to memory limitations, even though the curve has not plateaued.


The Sequential and BOW models represent two different classification problems.

if we set the threshold to 0.3, both t 1 and t 2 will be detected in the target sentence, and we found this to be a major source of undesirable insertions.


Both constraints have been shown to be in very good fit with data from dependency treebanks (Kuhlmann and Nivre, 2006).

with this notion of consistency, we can prove the following technical result about the relation between dependency structures and annotated trees.


Linearization proceeds in a way that is very close to a pre-order traversal of the tree, except that the relative position of the root node of a subtree is explicitly specified in the order annotation.

to linearize a tree t , we only need to linearize the first block of the tree's root node: lin.t/ D lin.


Sandy gave gave dog the dog gave bone a bone This paper does not consider stochastic dependency grammars directly, but see Section 8 for an application involving them.

the primary difference is that the Eisner/Satta algorithm involves two separate categories that are collapsed into the single category M here.


After all of the rolesets are considered, we are left with a filtered list of rolesets with their argument mappings, ordered by their syntactic similarity to the target verb.

the number of annotated instances in PropBank varies greatly from verb to verb; there are 617 annotations for the want roleset, only 7 for desire, and 0 for any sense of the verb yearn.


Using the same setting, the system achieves the performance of 91.02% in Accuracy in the same test set.

the main reason is that the tree kernel can capture effectively more structure features while the standard flat features can cover some other useful features, such as Voice, SubCat, which are hard to be covered by the tree kernel.


So the EM-based model tends to have preferences only for the "right" words.

each cell shows the wins minus losses for the method listed in the row when compared against the method in the column.


Three challenges must be met when adapting results established in English to another language: (1) acquiring high quality annotated data; (2) adapting the English task definition to the nature of a different language, and (3) adapting the algorithm to the new language.

in Hebrew, 59% are Nouns, Numbers or Proper Names.


This data has been used to evaluate most research since.

we would like to be able to identify these multi-head constructs properly, rather than simply treating them as a single entity (or even worse, as two different entities).


This significantly simplified and sped up the manual annotation process.

the annotation was performed by the first author.


The most common form of parser evaluation is to apply the Parseval metrics to phrase-structure parsers based on the Penn Treebank, and the highest reported scores are now over 90% (Bod, 2003;Charniak and Johnson, 2005).

and third, we provide the first evaluation of a widecoverage CCG parser outside of CCGbank, obtaining impressive results on DepBank and outperforming the RaSP parser  by over 5% overall and on the majority of dependency types.


RASP uses an unlexicalised parsing model and has not been tuned to newspaper text.

the simple punctuation rules used by the parser do not contain enough information to distinguish between the various cases of ta.


There are parser evaluation suites which have been designed to be formalism-independent and which have been carefully and manually corrected.

it is unclear whether these high scores accurately reflect the performance of parsers in applications.


In general, the estimation of p s (y|x) would be more influenced by the instances with high , then we should expect p t (x, y) to be very different from p s (x, y), and therefore different fromp s (x, y).

here we use only the labeled instances in D s but we adjust the weight of each instance by p t (x) ps(x) .


The obvious approach is to sample the states at each position i.i.d.

at test time, the POS tagging and parsing are done simultaneously by the parser.


The constraints violation penalty ρ is set to − log 10 −4 and − log 10 −1 for citations and advertisements, resp.

in the citations domain, H&W&C+i achieves with 20 labeled samples similar performance to the supervised version without constraints with 300 labeled samples.


When the constraint violation penalty ρ was infinity (equivalent to hard constraint), the accuracy improved to 78.7%, but when the penalty was set to −log(0.1), the accuracy of the model jumped to 80.6%.


Acquiring labeled data is a difficult and expensive task.

the results reported below are the averages over these 5 runs.


As in most state-of-the-art PBSMT systems, we use GIZA++ to obtain word-level alignments in both language directions.

we interpolate (log-linearly) the supertagged components (language model and phrase table) with the components of a standard PBSMT system.


Within the field of Machine Translation, by far the most dominant paradigm is Phrase-based Statistical Machine Translation (PBSMT) (Koehn et al., 2003;Tillmann & Xia, 2003).

in our model we employ the CCG supertagger to obtain the best sequences of supertags for a corpus of sentences from which we obtain language model statistics.


POS Tags vs. Supertags A supertag is a complex tag that localizes the dependency and the syntax information from the context, whereas a normal POS tag just describes the general syntactic category of the word without further constraints.

our efforts to build a system that benefits from the combination using a simple loglinear combination of the two models did not give any significant performance change relative to the baseline CCG system.


For the results we report, unless explicitly mentioned, all stated comparisons are statistically significant with 99.8% confidence.

finally, we note that a regressiontrained metric with the full features set that compares against 4 pseudo references has a higher correlation than BLEU with four human references.


When the reference location is anywhere else on the line segment, a relative distance to the reference does not uniquely specify a location on the line segment.

does varying the number and/or the quality of the references have an impact on the metrics?


In (Rosti et al., 2007), simple u ' f ¡ score was assigned to the word coming from the thbest hypothesis.

to address the issue with ungrammatical hypotheses and allow language model expansion and re-scoring, the hypothesis confidence computation is modified.


Third, the system weights are independent of the skeleton selection.

also, the METEOR score using the METEOR optimized weights is very high.


It has been argued that METEOR correlates better with human judgment due to higher weight on recall than precision (Banerjee and Lavie, 2005).

the other scores are worse in common with the tuning set results.


Our results also confirm the hypothesis -which has been attested in several corpus studies -that the order in the MF is rather rigid and dependent on grammatical properties.

(2004) do not make explicit, what their set of semantic relations consists of.


German is a verb second language, i.e., the position of the verb in the main clause is determined exclusively by the grammar and is insensitive to other factors.

in their simplest system, whose performance is only 2.5% worse than the performance of the best one, they condition on both syntactic categories and semantic relations (ψ) according to the formula: Although they test their system on German data, it is hard to compare their results to ours directly.


The results for the MF were the same as on the data from Wikipedia.

like τ , agr and inv can give a positive score to an ungrammatical order.


In generation, the surface realisation task consists in mapping a semantic representation into a grammatical sentence.

for instance, (Callaway, 2003) reports that the implementation of such a processor for SURGE was the most time consuming part of the evaluation with the resulting component containing 4000 lines of code and 900 rules.


It covers some 35 basic subcategorisation frames and for each of these frames, the set of argument redistributions (active, passive, middle, neuter, reflexivisation, impersonal, passive impersonal) and of argument realisations (cliticisation, extraction, omission, permutations, etc.)

from the NLG geared paradigm, it takes the ability to symbolically constrain the realiser output to a given generation context.


Fourth, the grammar can be used both to generate and to detect paraphrases.

it captures most grammatical paraphrases that is, paraphrases due to diverging argument realisations or to different meaning preserving alternation (e.g., active/passive or clefted/non-clefted sentence).


, r n ) of the semantic requirement, and there is one universal quantifier for y and for each parameter x j of the action except for ref(id(r)).

theoretically, our encoding provides a new framework for understanding and exploring the general relationships between language and action.


The notion of what constitutes fluency is an imprecise one, but trials with human judges have shown that even if it cannot be exactly defined, or even articulated by the judges, there is a high level of agreement about what is fluent and what is not.

we will look at predicting the fluency of near-human quality sentences.


Our approach combines aspects of both early and late fusion.

this gain is attained by identifying instances in which gesture features are especially relevant, and weighing their contribution more heavily.


We hypothesize that one way to reduce the user's cognitive load is to make explicit two pieces of information: the purpose of the current system turn, and how the system turn relates to the overall discussion.

the NM presence allows the users to better identify and follow the tutoring plan and to better integrate the instruction.


benefit from access to the discourse structure information.

implementing the NM in a new domain requires little expertise as previous work has shown that naïve users can reliably annotate the information needed for the NM (Passonneau and Litman, 1993).


Acknowledgements Thanks to Keith Bauer, Simon Byers, Harry Chang, Rich Cox, David Gibbon, Mazin Gilbert, Stephan Kanthak, Zhu Liu, Antonio Moreno, and Behzad Shahraray for their help and support.

the most common reported reasons were familiarity, physical comfort and ease of use.


Learning is local and parsing is (locally) greedy.

having defined a link-based representation of syntactic structure, it is natural to wonder what the relation is between this representation and standard dependency structures.


In this paper we provide a new evaluation of the efficacy of edge-factored models, k-best oracle results.

prior to this paper, an efficient non-projective k-best MST dependency parser has not been proposed.


As expected, this number decreases as T increases.

for applications such as IE and QA, which work at the single sentence level and which might reach erroneous decision due to an inaccurate parse, normalizing over sentence lengths is less of a factor.


Reputation Data: The second part of our data set contains the reputation history of each merchant that had a (monitored) product for sale during our 180-day window.

other work in this area includes (Lee, 2004;Popescu and Etzioni, 2005) which uses text mining in the context product reviews, but none uses the economic context to evaluate the opinions.


To do this an iterative algorithm is used that attempts to globally maximize the classification of all phrases while satisfying local consistency constraints.

in that work an initial model classified each sentence as being subjective or objective using a global mincut inference algorithm that considered local labeling consistencies.


The top subjective sentences are then input into a standard document level polarity classifier with improved results.

we reduce the problem of joint sentence and document level analysis to a sequential classification problem using constrained Viterbi inference.


Then we create a data set where each instance θx is labeled with the identity of the domain from which it came and train a linear classifier.

in our work we only show improvement for the basic model, but all of these new techniques also make use of lexical features.


The idea of using the regularizer of a linear model to encourage the target parameters to be close to the source parameters has been used previously in domain adaptation.

using the proxy A-distance as a criterion, we observe that we would choose one domain from either books or DVDs, but not both, since then we would not be able to adequately cover electronics or kitchen appliances.


He used the difference in mutual information with two human-selected features (the words "excellent" and "poor") to score features in a completely unsupervised manner.

we believe that our adaptation methods could be also applied to those more refined models.


Following that line, an entailment rule 'L → R' should be regarded as correct if in all (or at least most) relevant contexts in which the instantiated template L is inferred from the given text, the instan- If he is arrested, he can immediately seek bail.

for example, when answering the question "Which companies did IBM buy?"


Accurate learning of inference knowledge, such as entailment rules, has become critical for further progress of applied semantic systems.

it is therefore best to evaluate the rules using a corpus of the same domain from which they were learned, or the target application domain for which the rules will be applied.


These methods typically assume that a segmentation algorithm has access not only to acoustic input, but also to its transcript.

pattern Induction in Acoustic Data Our work is related to research on unsupervised lexical acquisition from continuous speech.


This alignment procedure may produce paths with high distortion subpaths.

baseline We use the state-of-the-art mincut segmentation system by Malioutov and barzilay (2006) as our point of comparison.


In a transcript similarity matrix shown in Figure 1 a), reference boundaries delimit homogeneous regions with high internal similarity.

this optimization problem can be solved using one of many unsupervised segmentation approaches (Choi et al., 2001;Ji and Zha, 2003;Malioutov and Barzilay, 2006).


boundaries along the span of the lecture and selecting random boundaries, respectively.

we trim each path to retain the subpath with lowest average distortion and length at least L. More formally, given an alignment of length N , we seek to find m and n such that: We accomplish this by computing the length constrained minimum average distortion subsequence of the path sequence using an O(N log(L)) algorithm proposed by Lin et al (2002).


We then evaluate the sub-sequence filtering approach to reducing the actual error rate of these models by adding both 3 and 4-grams from the Gigaword Corpus to the baseline.

a BF represents a set S = {x 1 , x 2 , ..., x n } with n elements drawn from a universe U of size N .


We take advantage of the monotonicity of the ngram event space to place upper bounds on the frequency of an n-gram prior to testing for it in the filter and potentially truncate the outer loop in Algorithm 2 when we know that the test could only return postive in error.

a Bloom filter (BF) is a randomised data structure for set membership queries.


Note that we use no pruning for these models and that the numbers of distinct n-grams is of the same order as that of the recently released Google Ngrams dataset (LDC2006T13).

we show that the Bloom filter (Bloom (1970); BF), a simple space-efficient randomised data structure for representing sets, may be used to represent statistics from larger corpora and for higher-order n-grams to complement a conventional smoothed trigram model within an SMT decoder.


The accuracies of these classifiers are shown in Table 5.

following Ponzetto and Strube (2006), we consider an anaphoric reference, NP i , correctly resolved if NP i and its closest antecedent are in the same coreference chain in the resulting partition.


Second, our model captures both global dependencies across different titles in the tree and local dependencies within sections.

also, the loop in line 1 of Figure 2 iterates over segment titles rather than words.


The language models are trained using the SRILM toolkit.

3 Step Two To train the global parameter vector α glob , we are given training examples (S i , T i ) for i = 1, .


The second assumption is similar to HITS if words and sentences are considered as authorities and hubs respectively.

the proposed approaches with different word similarity measures (WN and MI) are compared with two solid baselines: SentenceRank and Mutu-alRank.


In our experiments C(•, •) was a linear layer with discretized inputs (i − pos w , i − pos v ) which were transformed into two binary vectors of size wsz, where a bit is set to 1 if it corresponds to the position to encode, and 0 otherwise.


Because our network currently outputs labels on a per-word basis it is difficult to assess existing accuracy measures.

their system also uses a parser, as before, and then a polynomial Support Vector Machine (SVM) (Boser et al., 1992) is used in two further stages: to classify each node in the tree as being a semantic argument or not for a given verb; and then to classify each semantic argument into one of the classes (ARG1, ARG2, etc.).


The probability of a semantic relation r given feature pair f ij , P , is defined as the ratio between the number of occurrences of a relation r in the presence of feature pair f ij over the number of occurrences of feature pair f ij in the corpus.

lauer's set of 8 prepositions represents 94.5% (Europarl) and 97% (ClUVI) of the N P N instances.


Recent results have shown that symbolic noun compound interpretation systems using machine learning techniques coupled with a large lexical hierarchy perform with very good accuracy, but they are most of the time tailored to a specific domain (Rosario and Hearst, 2001).

this specialization procedure improves the system's performance since it efficiently separates the positive and negative noun-noun pairs in the WordNet hierarchy.


It is mostly true that English noun phrases translate into constructions of the form N P N in Romance languages where, as we will show below, the P (preposition) varies in ways that correlate with the semantics.

this semantic distribution contrasts with the one in CLUVI, which uses a more descriptive language.


Before showing the formula used for computing the word weights, we first introduce some notation: • Let X ∈ X be an arbitrary bag, and let X.a 1 and X.a 2 be the two arguments associated with the bag.

for example, consider the sentences S 1 and S 2 from the bag associated with "Google" and "YouTube" (as shown in figure 1).


Each dataset is split into two sets of bags: one for training and one for testing.

we also describe a method for weighting features in order to focus on those correlated with the target relation rather than with the individual entities.


We assume that the domain relevance of a pattern is dependent on the relevance of the lexical terms (words or collocations) constructing the pattern, e.g., the domain relevance of (5) and (6) are dependent on the terms "prize" and "win" respectively.

the ratio of events to reports is close to one.


The linguistic features of these anchors for the anchor types of Perpetrator, Action, Victim and Target for the MUC4 domain are given in Table 1.

here we want to evaluate given a single relation or relation path, whether the two anchors are correct in filling the appropriate slots in a template.


To overcome these differences, we construct candidate templates for all the domains and measure the required type of performance for each domain.

current semantic parsers such as the ASSERT are not able to recognize support verb constructions such as "X conducted an attack on Y" under the verb frame "attack" (Pradhan et al.


We show how to use corpus statistics and term extraction methods to correct the boundaries of relation arguments.

for example, the schema of the Acquisition relation Acquisition(Buyer=ProperNP, Acquired=ProperNP) ordered specifies that Acquisition has two slots, named Buyer and Acquired, which must be filled with entities of type ProperNP.


In this paper we show how to use these observations to supplement a Web RE system with an entity validation and correction component, which is able to significantly improve the system's accuracy.

this generality comes with the cost in accuracy.


For example, consider a sequence "Formerly Microsoft Corp.", which is produced by mistakenly labeling "Formerly" as a proper noun by the PoS tagger.

the Corpus-based validator is able to improve the results for the Inventor relation, which the other two validators are completely unable to do.


dependency forests or even general directed graphs.

all discussed edge measures take integer values: interval and component degrees take only nonnegative values, level types and level signatures take integer values (in all cases, their absolute values are bounded by the size of the whole dependency tree).


When applying self-training to a parser trained with a small dataset we expect the coverage of the parser to increase, since the combined training set should contain items that the seed dataset does not.

in spite of the strong dissimilarity between the domains, selftraining provides a substantial improvement.


We also applied the protocol of Steedman et al to scenario II with 500 randomly selected sentences, getting no improvement over the random baseline.

they did not explore the case of small seed datasets (the effort in manually annotating 20K is substantial) and their work addresses only one of our scenarios (OI, see below).


In this section we present our framework for syntactic parsing with dynamic Sigmoid Belief Networks.

the set of hidden variables H in our graphical model consists of all the state vectors S t , t ≤ t, and the last decision d t k .


The resulting estimate of the tree probability is given by: This approximation method replicates exactly the computation of the feed-forward neural network in (Henderson, 2003), where the above means µ t i are equivalent to the neural network hidden unit activations.

all the previously observed decisions h(t, k) comprise the set of visible variables V .


Undirected graphical models, in particular Condi-tional Random Fields, are the standard tools for shallow parsing (Sha and Pereira, 2003).

we propose two forms of approximation for dynamic SBNs, a neural network approximation and a form of mean field approximation .


All the parameters for both the NN and MF models were tuned on the validation set.

as we mentioned above, the feed-forward computation does not allow us to compute the optimal values of state means µ t i .


, d t n : where h(t, k) denotes the parsing history For example, a decision to create a new constituent can be divided in two elementary decisions: deciding to create a constituent and deciding which label to assign to it.

optimally, after each new decision d t k , we should recompute all the means µ t i for all the state vectors S t , t ≤ t. this would make the method intractable, due to the length of derivations in constituent parsing and the interdependence between these means.


For any source word s i , there are d i different transliterations made by the n i human transliterators ( where n i j is the number of times source word s i was transliterated into target word t i j ).

in all of our experiments, CA based metrics closely mirrored WA based metrics, and so conclusions drawn from the data would be the same whether WA metrics or CA metrics were used.


(2002) who recommend that four people should be used for collecting judgments for machine translation experiments.

we vary the number of human transliterators that are used to construct the corpus; and the origin of the English words used in the corpus.


In the transliteration generation stage, the source word is segmented following the same process of segmenting words in training stage, and a probability is computed for each generated target word: where |K| is the number of distinct source segments.

the process of aligning words explained above can handle words with already known components in the alignment set A (the frequency of occurrence is greater than zero).


This method shows further 7.1% to 8.1% increase in word accuracy (TOP-1) with our new alignment algorithm.

for the example "shelley", the first segment is "sh" which belongs to C pattern.


The pair (sutoresu, stress) can be aligned: For the feature representation, we only extract substring pairs that are consistent with this alignment.

cognates arise either from words in a common ancestor language (e.g.


String similarity is a fundamental concept in a variety of fields and hence a range of techniques have been developed.

the errors that our system makes are often either linguistically interesting or point out mistakes in our automatically-labelled bitext and (to a lesser extent) dictionary data.


For CL-CE, using named information achieves the best result among the three configurations.

we use the "n+" feature configuration as in Section 4.4, that is, names plus POS filtered items.


The distance between the test document and a cluster can then be easily measured by the cosine similarity function as in Equation (1).

using finer-grained multiple topics in dynamic adaptation improves system performance.


In our experiments, an actual feature might be the word at position 5 is 'PC' and the current tag is AUC.

how to effectively process the data and make it suitable for natural language processing becomes a challenging issue.


Redundancy-based approaches to pattern-based IE assessment  require that extractions appear relatively frequently with a limited set of patterns.

in the state space employed by the HMM, the contexts in phrases (i) and (ii) are in fact quite similar, allowing HMM-T to detect that Pickerington and Chicago are likely of the same type.


Following Och and Ney (2002), we base our model on log-linear framework and adopt the seven feature functions described in (Liu et al., 2006).

we selected 571 short sentences from the 2002 NIST MT Evaluation test set as our development corpus, and used the 2005 NIST MT Evaluation test set as our test corpus.


This usually results in very complex auxiliary rules, especially on real-world training data, making both training and decoding very slow.

we use the empty string "" to denote the pseudo root of a forest.


The extraction algorithm is shown in Figure 2.

the performance of linguistically syntax-based models can be hindered by making use of only syntactic phrase pairs.


Taking the lexicalization an intuitive a step forward, we propose a novel, finer-grained solution which models the content and context information encoded by function words -approximated by high frequency words.

we develop the three sub models (see Section 3) which influence the choice of production rules for ordering decision.


On the one hand, global reordering, which cannot be accomplished by the Input , Meanwhile , Yushchenko and his assistants discussed issues concerning the establishment of a new government Baseline The same time , Yushchenko assistants and a new Government on issues discussed Translation with Preprocessing The same time , Yushchenko assistants and held discussions on the issue of a new government Table 6: Translation Example 2 phrase-based model, is enabled by the tree operations in preprocessing.


A variety of reordered SL sentences are fed to the decoder so that the decoder can consider, to certain extent, the interaction between reordering and other factors of translation.

some ideal translations exhibit reorderings longer than such distortion limit.


Comparative statistics of a standard and a triangulated phrase-table are given in Table 4.

5 These results indicate that triangulation is effective for disjoint bitexts, although ideally we would test this with independently sourced parallel texts.


triangulated phrase-table with the standard sourcetarget one.

the phrasal overlap is much greater (10 to 20%), which allows for triangulation using these common phrases.


The only complication is that resampling a tag changes the identity of three trigrams at once, and we must account for this in computing its conditional distribution.

perhaps the most well-known is that of Merialdo (1994), who used MLE to train a trigram hidden Markov model (HMM).


For POS tagging, estimates based on multiple samples might be useful if we were interested in, for example, the probability that two words have the same tag.

likewise, modal verbs and the copula are mostly separated from other verbs.


In our initial experiments, we experimented with different fixed values of the hyperparameters and reported results based on their optimal values.

typically, this is done using maximum-likelihood estimation (MLE) of the model parameters.


We obtain an error rate of 2.67% on the test data.

on set A, Ratnaparkhi's feature set, our system reports an error rate of 3.05% on the development data set.


Hence, we infer that both the answer's PT and BOW features are very useful in the classification task.

we have defined tree structures (PAS and PASN) to represent predicateargument relations, which we automatically extract using our SRL system.


Note that dialogue generation is constrained to contain certain fixed actions that are essential for task completion (e.g.

in order to build a new dialogue system, dialogue system engineers will have to set up a rough dialogue template containing several 'multiple choice'-action nodes.


Additionally, the higher degree of inter-subject agreement in the contextualized condition, along with the strong correlations found for contextualized features, suggests that context, when available, plays a central role in the disambiguation of okay.

results of a perception study show that contextual cues are stronger predictors of discourse function than acoustic cues.


In all cases, tokens which subjects heard in context showed more agreement than those they heard in isolation.

these results suggest that contextual features might override the effect of most acoustic, prosodic and phonetic features of okay.


In a previous corpus-study, using transcriptions of spontaneous, task-oriented and non-task-oriented dialogue, utterances were annotated with syntactic trees, which we then used to determine the phrasestructure rules that licensed production (and comprehension) of the utterances (Reitter et al., 2006b).

prior work on predicting task success has been done in the context of human-computer spoken dialogue systems.


Thus we define the strength of priming as the decay rate of repetition probability, from shortly after the prime to 15 seconds afterward (predictor: DIST).

switchboard contains spontaneous conversation over the telephone, while the task-oriented Map Task corpus was recorded with interlocutors co-present.


There is considerable interest in learning computational grammars.

figure 3: A definition-type sentence and its ontology-based representation obtained using our learned LWfG matching problem where the "wh-word" matches the answer concept).


Figure 1 gives the algorithm, where N is the number of training sentences and T is the number of passes over the data.

human readers often use semantics, contextual information about the document and world knowledge to resolve segmentation ambiguities.


For example, " Ä" could be treated as a single word (Bank of Beijing) for machine translation, while it is more naturally segmented into " (Beijing) Ä (bank)" for tasks such as text-to-speech synthesis.

several discriminatively trained models have recently been applied to the CWs problem.


(Except for the last example in the last iteration, when each dimension of τ is updated, no matter whether the decoder output is correct or not).

the perceptron model allows a wide range of features, and so future work will consider how to integrate open resources into our system.


This aspect of nominal mentions is entirely unmodeled in our system.

one then applies discriminative learning methods to pairs of mentions, using features which encode properties such as distance, syntactic environment, and so on (Soon et al., 2001;Ng and Cardie, 2002).


There are two reasons to adopt the softer approach.

there is a natural penalty for each cluster which is actually used.


For example, when only 5,000 French-Spanish sentence pairs are available, the interpolated model outperforms the standard model by achieving a relative improvement of 17.55%, with the BLEU score improved from 0.2747 to 0.3229.

this model may perform poorly due to the sparseness of the data.


Levenshtein distance depends on finding identical letters, so both words must use the same alphabet.

the process is repeated until convergence.


They also confirm the observation by Evert and Baroni (2006) that current LNRE models achieve only unsatisfactory prediction accuracy, and this is the issue we turn to in the second part of the paper (Section 4).

7 A table with the full numerical results is available upon request; we find, however, that graphical summaries such as those presented in this paper make the results easier to interpret.


The larger X 2 , the poorer the training set fit; the larger rMSE, the worse the prediction.

focusing for now on the circles (pinpointing the ZM, fZM and GIGP models), we see that there is instead a negative correlation between goodness of fit on the training set and quality of prediction on unseen data.


We see from this figure that the performance of both fZM and GIGP improves dramatically when they are trained and tested on randomized sequences of words.

we can ignore the issue of what is the boundary between topical words to be echo-adjusted and general words, as long as we can be confident that the set of lowest frequency words used for model fitting belong to the topical set.


One might argue that unsupervised algorithms are not an interesting option from the engineering point of view, because rule-based systems usually lead to better results.

when we ran the original algorithm on a German data set, no suffixes were validated but reasonable prefix lists were found.


As can be seen from the equation above, this probability vector is an average of the expected behavior h 0 and the observed data d, weighted by β and the amount of observed data |D|, respectively.

it is generated from the "expected" behavior, h 0 , which is an average of the known behavior from a random sample of other lemmas.


We assume the multinomial distribution, whose parameter is simply the vector of probabilities of each IPS.

grammars and dictionaries of Russian frequently cite paradigmatic gaps in the 1sg non-past.


By his logic, there must therefore be some source of grammar conflict which prevents speakers from generalizing.

yet the gaps persist in part because of analogical pressure from existing gaps.


The transliteration transducer is split into three states to allow mapping probabilities to be learned separately for letters at the beginning, middle and end of a word.

the Viterbi substring decoder is able to encode contextual information in the transliteration model because of its ability to consider larger many-to-many mappings.


The 0.4 percent F-score improvement is significant at p < 0.001. ing the parser more time to generate the parses.

we derive these base-phrase constraints from three sources: the reranking stage of the parsing pipeline; a finite-state shallow parser (Hollingshead et al., 2005); and a combination of the output from these two sources.


Rule extraction is forbidden in this case because it would destroy the link between bordering and next to.

for each rule, r, there is a feature that returns the number of times r is used in a derivation.


More research is needed in order to know exactly what the dependencies are between parsing strategy and tree transformations.

again, it is a different question whether the transformations have a positive effect for all treebanks (languages) to which they can be applied.


companied by a even higher increase for SDT.

there is no correlation if information concerning the lifts are not added to the labels.


The second generates a subjectivity-annotated corpus in a target language by projecting annotations from an automatically annotated English corpus.

the multi-word expressions that are not validated in this process are discarded, reducing the number of expressions from an initial set of 990 to a final set of 264.


On the general ranking scale, there was little agreement (kappa = 0.1685), corroborating feedback from respondents on the difficulty of providing a general rating for text polarity distinct from a rating with respect to one of the two companies.

this option was included to test for possible adverse effects of the lexicon.


This effect is illustrated in Table 2, and from this analysis we chose α = 5 as an appropriate level of smoothing.

the first author is supported by an University of Cambridge Millennium Scholarship.


Given a collection of sentences, S, the task is to label each sentence as either speculative or nonspeculative (spec or nspec henceforth).

the training and classification models we have presented require the setting of two parameters: the smoothing parameter α and the number of features per sample m. Analysis of the effect of varying α on feature ranking reveals that when α = 0, low frequency terms with spurious class correlation dominate and as α increases, high frequency terms become increasingly dominant, eventually smoothing away genuine low-to-mid frequency correlations.


Working on this premise, all features are ranked according to their probability of 'hedge cue-ness': which can be computed directly using (4) and (6).

we used the following procedure to obtain a set of nspec seeds: 1.


As our goal is to identify multimodal cues of segmentation in face-to-face conversation, we use the AMI meeting corpus (Carletta et al., 2006), which includes audiovideo recordings, to test our approach.

for example, interlocutors do not move around a lot when a new discussion is brought up; interlocutors mention agenda items (e.g., presentation, meeting) or content words more often when initiating a new discussion.


As essays and descriptions of professions display a mismatch between the vocabularies of topics and documents and there is lack of contextual information, due to the documents being fairly short as compared to standard IR scenarios, lexical semantic information should be especially beneficial to an IR system.

this result indicates that IR-2 utilizing conventional information retrieval techniques and semantic information from Wikipedia is better suited for longer queries providing enough context.


This work was supported by the German Research Foundation under grant "Semantic Information Retrieval from Texts in the Example Domain Electronic Career Guidance", GU 798/1-2.

they only indicate the approximate difficulty of the task.


It traverses the document from left to right and uses a mention-synchronous approach to decide whether a mention should be merged with an existing entity or start a new entity.

whereas the similarity model may be reasonably good at predicting the distance between two pronouns that are close to each other, it is not good at predicting the distance between pronouns that are furthur apart.


In order to make sure that the process is as language-independent as possible, we declined to make use of any non-English linguistic resources outside of the Wikimedia domain (specifically, Wikipedia and the English language Wiktionary (en.wiktionary.org)).

of course, even in languages with capitalization, "train station" would be marked incorrectly in the article in which the above was located, but the mistake would be isolated, and should have minimal impact overall.


The main processing of each article takes place in several stages, whose primary purposes are as follows: • The first pass uses the explicit article links within the text.

we did not use any semantic resources such as WordNet or part of speech taggers.


These simple counts strongly indicate whether another noun can replace the pronoun.

these approaches, like ours, are ways to inject sophisticated "world knowledge" into anaphora resolution.


The manual analysis of the sample of 200 class labels indicates that 154 (77%) are relevant concepts and 27 (13.5%) are subjectively relevant concepts, for a total of 181 (90.5%) relevant concepts, whereas 19 (9.5%) of the labels are incorrect.

the instance sets are not manually postfiltered or otherwise changed in any way.


they make parsing decisions based on n-gram statistics computed for specific lexemes).

to augment the size of our corpus, we used the same technique to collect data for two additional relations, and manually labelled positive and negative instances by hand over all collections.


The following experiments demonstrate the benefits of Open IE for two tasks: open extraction and targeted extraction.

in this case, one must be willing to undertake the cost of acquiring labeled training data for each relation, either via a computational procedure such as bootstrapped learning or by the use of human annotators.


Also, metrics such as METEOR determine an alignment between the items of a sentence pair by using heuristics such as the least number of matching crosses.

the ACL-07 Mt workshop evaluated the translation quality of Mt systems on various translation tasks, and also measured the correlation (with human judgement) of 11 automatic Mt evaluation metrics.


Once again, we find matches in a left-to-right fashion.

a system unigram (l In the case of bigrams, the matching conditions are l s i = l r i and l s i+1 = l r i+1 .


The projected spans for the local tree rooted at begins in each candidate are shown in Table 1.

this algorithm was used to implement a soft cohesion constraint for the Moses decoder, based on a source-side dependency tree.


Practically it has been proved to work well.

iBM Model-1 is simple yet has been shown to be effective in many applications (Och et al., 2004).


For example, in above translation, the phrase translation table may suggest the word three be translated into "三", "三本", "三只", etc, and the word books into "书", "书本", "名册" (scroll), etc.

due to the limited size of bilingual corpora, many measure words, as well as the collocations between a measure and its head word, cannot be well covered by the phrase translation table in an SMT system.


Once the head word is determined, the collocated measure word can usually be selected accordingly.

this also indicates that source information without noises is helpful for measure word generation.


Another potential option would be Gibbs sampling (or some other sampling technique).

specific to our ITG case, the M step becomes: where ψ is the digamma function (Beal, 2003), s = 3 is the number of right-hand-sides for X, and m is the number of observed phrase pairs in the data.


Further, the formalism allows to express complex linguistic constraints by means of predicates or relational constraints.

the evaluation scheme was taken from Mctait and Adda-Decker (2003).


Only 25 of the 44000 hypotheses 2 caused an early termination of the parser due to the imposed memory limits.

we do not make this distinction but rather let immediate dominance schemata specify constituent order.


Given a set of low level features that correlate with the basic events in sports, we can now focus on building up representations of complex events.

these games represent data totaling approximately 275 hours and 20,000 distinct events from 25 teams in 23 stadiums, broadcast on five different television stations.


In such models, the meaning of a word is defined by its relationship to representations of the language users' environment.

in future work, we will examine the strengths and limitations of grounded language modeling in these domains.


not at a syllable boundary) often restrict the adjacent context, violating conditional independence given ¬b.

this sets a much higher standard for models of child language acquisition and also suggests that it is not crazy to speculate about inserting such an algorithm into the speech recognition pipeline.


For example, we must explain why English speakers consider "the" to be a word, but "-ing" to be an affix, although neither occurs by itself in fluent prepared English.

wordEnds keeps affixes on words whereas Goldwater's algorithm removes them.


The results for short keyword queries are shown in Table 3.

we can compute the term semantic similarity based on synset definitions in the following way: where D(t) is the concatenation of the definitions for all the synsets containing term t and |D| is the number of words of the set D. Within a taxonomy, synsets are organized by their lexical relations.


Our empirical results on six TREC collections show that query expansion using only hand-crafted lexical resources leads to significant performance improvement.

the computational cost is high and the benefit of query expansion using only WordNet is unclear.


Rule-based approaches are intuitive and easy to implement.

the results show that the two methods we propose both outperform the original queries significantly with less than two alterations per query on average.


Therefore, we can select at most one optimal alteration for the whole query.

in this section, we introduce a method belonging to supervised learning family.


Another drawback of stemming is that it usually enhances recall, but may hurt precision (Kraaij and Pohlmann, 1996).

if many alterations are added, query traffic will be greatly increased.


In comparison, the Bigram Expansion model performs better than the Similarity model.

attempts have been made to use corpus analysis to improve existing rule-based stemmers.


We employ the framework of language modeling (for information retrieval) to develop our approach to question search.

note that only the questions occurring in the test set can be evaluated.


In the language modeling approach to information retrieval, the relevance of a targeted question to a queried question is given by the probability | of generating the queried question Following the framework, we propose a mixture model for modeling question structure (namely, question topic and question focus) within the process of searching questions: In the mixture model, it is assumed that the process of generating question topics and the process of generating question foci are independent from each other.

the evaluation was conducted with exactly the same setting as in Section 3.


From Figure 3, we see that our approach performs best when λ is around 0.7.

we empirically conduct the question search with questions about 'travel' and 'computers & internet'.


Our approach follows Langkilde-Geary (2002) and Callaway (2003) in aiming to leverage the Penn Treebank to develop a broad-coverage surface realizer for English.

the main difference between the approaches is that ours consists of a multitagging step followed by the bottomup construction of a realization chart, while theirs involves the top-down selection of the single most likely supertag for each node that is grammatically compatible with the parent node, with the probability conditioned only on the child nodes.


(2005) and Cahill and van Genabith (2006) with HPSG and LFG grammars.

the hypertagger makes possible a more than 6-point improvement in the overall BLEU score on both the development and test sections, and a more than 12-point improvement on the sentences with complete realizations.


Although there is no direct relationship between BLEU and likelihood, it provides a rough measure for comparing performance.

d: why we want the that matters we to be referred back.


In order to maximize BLEU score using the algorithm described in Section 4, we need a sizable trigram forest as a starting point.

this technique, together with the progressive search at previous stages, gives a decoder that produces the highest BLEU score we have obtained on the data in a very reasonable amount of time.


With this heuristic, we achieve the same BLEU scores and model cost as a trigram decoder with essentially the same speed as a bigram decoder.

we are focusing on Inversion Transduction Grammars (Wu, 1997) which are an important subclass of SCFG.


For each parameter we have estimated its desired range using the (Nastase and Szpakowicz, 2003) set as a development set.

once we have feature vectors, we can use a variety of classifiers (we used those in Weka) to construct a model and to evaluate it on the test set.


As observed by participants of Task 4, we can see that different sets vary greatly in difficulty.

numerous methods have been devised for classification of semantic relationships, among which those holding between nominals constitute a prominent category.


The lowest correlation (ρ = 0.04) is observed for the simple additive model which is not significantly different from the non-compositional baseline model.

in this paper we presented a general framework for vector-based semantic composition.


The merits of different approaches are illustrated with a few hand picked examples and parameter values and large scale evaluations are uniformly absent (see Frank et al.

since the product of zero with any number is itself zero, the presence of zeroes in either of the vectors leads to information being essentially thrown away.


To this end, we optimized the weights on a small held-out set.

relaxing the assumption of symmetry in the case of the simple additive model produces a model which weighs the contribution of the two components differently: This allows additive models to become more syntax aware, since semantically important constituents can participate more actively in the composition.


Henceforth, we will use only the approximate hierarchical model in our experiments and discussion.

in the next section we address the problem of how to come up with a suitable prior for transfer learning across named entity recognition problems.


Specifically, we have shown that hierarchical priors allow the user enough flexibility to customize their semantics to a specific problem, while providing enough structure to resist unintended negative effects when used inappropriately.

instead, we might want to learn the related feature L.1.Dr.


Inspired by the observation about trigger distribution, we propose a similar hypothesis -one argument role per cluster for event arguments.

in the following example, The Davao Medical Center, a regional government hospital, recorded 19 deaths with 50 wounded.


This material is based upon work supported by the Defense Advanced Research Projects Agency under Contract No.

for a collection of topically-related documents, the distribution may be much more convergent.


Finally, considering the document alone, without the image yields inferior performance.

furthermore, in order to develop robust image annotation models, it is crucial to have large and diverse datasets both for training and evaluation.


They showed that, depending on the domain, a small number (e.g., 50) of labeled examples allows to adapt the model learned on another corpus to a new domain.

many applications require reliable processing of heterogeneous corpora, such as the World Wide Web, where the diversity of genres and domains present in the Internet limits the feasibility of in-domain training.


6 Integrating the Corpus-based and Dictionary-based Approaches The strategy of integration of two or more systems in a single ensemble of classifiers has been actively used on different tasks within NLP.

the lexiconbased approach is characterized by a bounded but stable performance when the system is ported across domains.


For the second model we trained a maximum entropy classifier, one per each aspect, using 10-fold cross validation and unigram/bigram features.

it is used to produce a sample from a joint distribution when only conditional distributions of each variable can be efficiently computed.


Each classifier is explicitly associated to a single topic in the model and only words assigned to that topic can participate in the prediction of the sentiment rating for the aspect.


These are instead determined by syntactic, semantic, and pragmatic factors, such as transitivity, word order, animacy, gender, social prestige, and referential specificity (Finnemann, 1982, a.o).

for example, >B can be derived as in (48): The D rules are also theorems of this system.


Downstream NLP components can now exploit the crucial information in NP structure.

when a conjunction occurs in an NP, a non-CCG rule is required in order to reach a parse: (3) This rule treats the conjunction in the same manner as a modifier, and results in the incorrect derivation shown in Figure 1(a).


It may seem trivial to process in this case, but consider a similarly structured NP: lower court ruling that the U.S. can bar the use of... Our minimalist approach avoids reanalysing the many linguistic constructions that can be dominated by NPs, as this would reinvent the creation of CCGbank.

instead, all NPs were made rightbranching, as shown in this example: This structure is correct for most English NPs and is the best solution that doesn't require manual reannotation.


The second issue is the possible exponential blowup of the number of generated sentences.


This formula is positive when the expected value of the l th feature is higher on the set of correct simple labelings K sv than on the set of all simple labelings C sv .

the insertion of the phrase "a sandwich" does not prevent the rule from matching.


Figure 4 shows the depassivizing rule and the result of applying it to the sentence "I was given a chance."

these systems all use information from multiple parses, allowing them to fix many errors caused by incorrect parses.


This table shows that Page-Rank has a lower accuracy than that of CWS and the difference is significant in all four cases.

the higher accuracy of CWS may suggest that people tend to use the same words to communicate in email conversations.


Figure 1(b) shows the fragment quotation graph of the conversation shown in Figure 1(a) with all the redundant edges removed.

moreover, we study how to include subjective opinions to help identify important sentences for summarization.


In those cases, the authors are usually professional in writing and choose their words carefully, even intentionally avoid repeating the same words to gain some diversity.

p R(s) is the page-Rank score of a node (sentence) s. d is the dumping factor, which is initialized to 0.85 as suggested in the page-Rank algorithm.


• OpBear: The list of opinion bearing words in (Kim and Hovy, 2005).

the GSValue of a sentence ranges from 0 to 15 (5 human summarizers x 3).


Not only are errors inherently undesirable for obtaining an accurate grammar, but training on data with erroneous rules can be detrimental to parsing performance (e.g., Dickinson and Meurers, 2005;Hogan, 2007) As annotation schemes are not guaranteed to be completely consistent, other ad hoc rules point to non-uniform aspects of the annotation scheme.

rules without such evidence are likely ad hoc.


Figure 1 depicts the lattice for a 2-words sentence bclm hneim.

gL is then used to parse the string tn 1 .


There is no longer an effect for final repetitions or words after repetitions; all other disfluency features increase the odds of an error by a factor of 1.3 to 2.9.

this figure illustrates some of the dif-  ferences between two speakers chosen fairly arbitrarily from our data set.


In the previous section, we investigated the effects of various individual features on ASR error rates.

overall, the results from our joint analysis suggest   that, after controlling for other factors, extreme values for prosodic features are associated with worse recognition than typical values.


We also see a tendency towards higher IWER for very slow speech, consistent with Shinozaki and Furui (2001) and Siegler and Stern (1995).

pitch and intensity range show clear linear trends, with greater range of pitch or intensity leading to lower IWER.


The paper also includes a discussion of challenges in name translation evaluation.

consider for example the Arabic phrase The BBN annotators adopted the correct translation Termoli, but also the incorrect Portran.


Z @ P P @ (as a whole phrase) to "Premier Li Peng", based on its bitext knowledge.

we train our system on a corpus of 6 million stat sentences, and 500; 000 core training sentences.


, G Bn ) is the distribution over trees with root label A satisfying: TD A (G 1 , .


The inner-most routine should compute: for each class c, where is a normalizing constant.

in the dataset, 1,174 newspaper articles are annotated with 8 NE categories: ARTiFACT, DATE, LO-CATiON, MONEY, ORGANiZATiON, PERCENT, PERSON, and TiME.


(2006) used gazetteers with about 120,000 entries in total, and Nadeau et al.

if we use MPI for parallelization, the parallelized version of this routine should resemble the algorithm shown in Figure 1.


Essentially a concept is a grouping of words from either resource.

all three perform fairly well compared to the baseline Simple method.


Many terms occur both in the 1911 and 1987 Thesauri, but many more are unique to either.

were we simply to have selected a value somewhere in the middle of the graph, as was our original intuition, it would have given an unfair advantage to either version of Roget's Thesaurus over Word-Net.


Clearly, their method is supervised because it requires the full-abbreviation relations as training data.

our method does not require any additional annotated data other than the data that a regular translation system uses.


One may use a named entity tagger to obtain such a list.

the feature functions are combined under a log-linear framework, and the weights are tuned by the minimum-error-rate training (Och, 2003) using BLEU (Papineni et al., 2002) as the optimization metric.


For example, they may appear in the same sentence, or in the neighborhood sentences.

since most of statistical translation models (Koehn et al., 2003;Chiang, 2007;Galley et al., 2006) are symmetrical, it is relatively easy to train a translation system to translate from English to Chinese, except that we need to train a Chinese language model from the Chinese monolingual data.


Assuming that Levin's analysis is generally applicable across languages in terms of the linking of semantic arguments to their syntactic expressions, these mixed feature sets are potentially useful for building verb classifications for other languages.

previous studies on AVC have focused on using SCFs.


Many scholars hypothesize that the behavior of a verb, particularly with respect to the expression of arguments and the assignment of semantic roles is to a large extent driven by deep semantic regularities (Dowty, 1991;Green, 1974;Goldberg, 1995;Levin, 1993).

we therefore decide to break it into two individual dependency relations: PP(with), PP-fork.


In addition to the lexical part, for each extracted feature, we keep a direction.

this "inferred" accuracy could be misleading, and the LRA could have performed better if it was restricted to solve noun-only analogies, which seem easier than the general ones, as demonstrated by the significant increase in accuracy for LRA when limited to nouns: 67.4% vs. 56%.


When working with multiple transcriptions of the same content, we are again presented with new opportunities.

first, rather than only the Normal distribution, the response Y i is free to have any distribution belonging to the exponential family of distributions.


What-is-said features are better, but only barely so within the usual operating region of summarizers.

6 We conducted the same evaluation without splitting the lectures into 8-15 minute segments (so that the summaries summarize an entire lecture), and although space here precludes the presentation of the ROC curves, they are nearly identical Figure 4: ROC curve for utterance selection with the lecture corpus with several feature combinations.


dress, wear) still may identify important utterances.

as with most work in speech summarization, our strategy involves considering the problem as one of utterance extraction, which means that we are not synthesizing new text or speech to include in summaries, nor are we attempting to extract small phrases to sew together with new prosodic contours.


This confirms our hypothesis that users prefer summary responses when they are unfamiliar with the domain, but disconfirms the hypothesis that users will prefer summaries based on a user model.

we use two attributes to produce associative clusters, as shown in Fig.


The probability p(r|c, f, i) is the total probability of all paths from the top-most WordNet concept entity to the terminal node r. The model is trained on verb-argument tuples of the form described above, i.e., consisting of a verb and a subcategorisation frame, plus the nominal 2 heads of the arguments.

the table cells provide the log e of the probabilities per tuple token.


To the best of our knowledge, Schulte im Walde (2006) is the only hard-clustering approach that previously incorporated selectional preferences as verb features.

the smoothing ensures that the verb class model assigns a positive probability to each verb-argument tuple with a known verb, a known subcategorisation frame, and arguments which are in WordNet.


her model was not soft-clustering, and she only used a simple approach to represent selectional preferences by WordNet's top-level concepts, instead of making use of the whole hierarchy and more sophisticated methods, as in the current paper.


Querying each of these arrays for each n-gram requested would be inefficient and inflate the error rate since a false positive could occur on each individual array.

backoff smoothing algorithms typically request the longest n-gram supported by the model first, requesting shorter n-grams only if this is not found.


The composite hash function is made up of k independent hash functions h 1 , h 2 , .

many of our findings should transfer to other applications of language modeling.


A key benefit of the approach is that a straightforward training algorithm can be used to obtain global solutions.

another approach, generative probabilistic models, are a well-studied framework that can be extremely effective.


(2004) one can show that minimizing the objective (3) is equivalent to solving the quadratic program where e denotes the vector of all 1's and ξ represents slack variables.

most previous work that has attempted semi-supervised or unsupervised approaches to parsing have not produced results beyond the state of the art supervised results (Klein and Manning, 2002;.


Firstly, we employ the syllable-based search to revise the transliteration candidates from the statistical model.

in this paper, we mainly focus on the following two problems to be solved in transliteration.


In this paper we have presented a study of the performance of a state-of-the-art SRL system trained on two alternative codifications of roles (PropBank and VerbNet) and some particular settings, e.g., including/excluding verb-specific information in features, labeling of infrequent and unseen verb predicates, and domain shifts.

the role set is theoryneutral and consists of a set of numbered core arguments (Arg0, Arg1, ..., Arg5).


In the second setting ('CoNLL setting' row in the same table) the PropBank classifier degrades slightly, but the difference is not statistically significant.

section 3 presents the experimental setting and the base sRL system used for the role set comparisons.


The proposed model adopts tree sequence 1 as the basic translation unit and utilizes tree sequence alignments to model the translation process.

4)  treat all bilingual phrases as lexicalized tree-to-string rules, including those non-syntactic phrases in training corpus.


There is also a distinguished root node TOP in each forest, denoting the goal item in parsing, which is simply S 0,l where S is the start symbol and l is the sentence length.

we miss the benefits of non-local features that are not representable here.


Finally, we found it useful to impose a form of vocabulary restriction on the cluster-based features.

in Table 7, we show the development-set performance of second-order parsers as the threshold for lexical feature elimination (see Section 3.2) is varied.


For occurrences of links x1 , x2 , the element of π at a row addressed by x 1 and a column addressed by x 2 is 1 if x 1 = x 2 , and 0 otherwise.

we conclude that function KARIZE() takes time O(L 3 ) for each synchronous tree γ, and the total running time is O(|G| , where Y is the set of synchronous tree pairs of G. The term |G| accounts for the reading of the input, and dominates the complexity of the algorithm only in case there are very few links in each synchronous tree pair.


While POS taggers such as TreeTagger are common, and there some supertaggers are available, notably that of Clark and Curran (2007) for CCG, no standard supertagger exists for HPSG.

we use TreeTagger (Schmid, 1994) to produce POS tags and then open class words are restricted if the POS tagger assigned a tag with a probability over a certain threshold.


An example of such an application is the real-time speech translation system developed in the Verbmobil project (Wahlster, 2000), which integrated deep parsing results, where available, into its appointment scheduling and travel planning dialogues.

in many application scenarios, this is unnecessary and time consuming.


When applying AMR on the minus-one-model cross validation results, we see similar results that the ranking model reproduces hu-  Table 8: AMR Scores for Regular Cross Validation man judges' rankings.

since our testing corpus is relatively small, we would like to confirm this result on a large corpus and on other dialog systems in the future.


Rather than Markov Decision Processes (MDPs), partially observable MDPs (POMDPs) potentially provide a much more powerful framework for robust dialog modeling since they consider nbest hypotheses to estimate the distribution of the belief state (Williams and Young, 2007).

following that, we explain greedy selection with n-best hypotheses in Section 5.


To avoid these errors, a basic solution is to improve the accuracy and robustness of the recognition and understanding processes.

if NEXT TASK is selected, the current node is pushed to the focus stack.


The node selection is determined by calculating some score functions.

difficulties occur when attempting to de-ploy EBDM in practical spoken dialog systems in which ASR and NLU errors are frequent.


In general, errors in spoken dialog systems are prevalent due to errors in speech recognition or language understanding.

v corresponds to user goal state to achieve domain-specific subtask in its expected agenda.


This independence from large amounts of in-domain dialogue data allows researchers to apply RL to new application areas beyond the scope of existing dialogue systems.

linear function approximation learns linear estimates for expected reward values of actions in states represented as feature vectors.


We learn with the decision tree J4.8 classifier, WEKA's implementation of the C4.5 system (Quinlan, 1993), and rule induc-baseline JRip J48 timing 52.0(± 2.2) 50.2(± 9.7) 53.5(±11.7) modality 51.0(± 7.0) 93.5(±11.5)* 94.6(± 10.0)* Table 1: Predicted accuracy for presentation timing and modality (with standard deviation ±), * denotes statistically significant improvement at p < .05 tion JRIP, the WEKA implementation of RIPPER (Cohen, 1995).

all other actions are left for optimisation.


We face the problem of learning such models when training data is sparse.

to this preceding work, our approach enables strategy learning in domains where no prior system is available.


We used first order HMMs for embedded joint PMs since we assume that they have the same graph structure as JESS-CM as described in Section 2.2.


In some cases, these tasks have relatively large amounts of labeled training data.

with a generative approach, a well-known way to achieve this incorporation is to use maximum marginal likelihood (MML) parameter estimation, i.e., (Nigam et al., 2000).


The evidence provided by the above experiments implies that increasing the coverage of unlabeled data offers the strong possibility of increasing the expected performance of unseen data.

our results require no such additional knowledge or limitation.


A popular method, the so-called distributional similarity, is based on the dictum of Zelig Harris "you shall know the words by the company they keep": given highly discriminating left and right contexts, only words with very similar meaning will be found to fit in between them.

we estimate the quality of paraphrases by annotating a random sample as correct/incorrect and calculating the accuracy.


For example, if one wanted to extract a list of company acquisitions, " ACQUIRER acquired ACQUIREE " would be one surface pattern with " ACQUIRER " and " ACQUIREE " as the slots to be extracted.

so an increase in the number of input seeds, is likely to increase the relative recall of the resource.


To implement the CP framework, concrete models are needed for each component, specifying its representation, how it is constructed, and an appropriate matching procedure.

when ignoring the prior rule score, cp(r, t) is the major contributor over the baseline Precision.


h: 'X invented the PC' for "Who invented the PC?").

h should not be matched in "Engineers demonstrated the new system", due to a mismatch between the intended sense of 'demonstrate' in h and its sense in t. Similarly, when looking for physical attack mentions using the hypothesis 'X attack Y ', we should not utilize the rule r: 'X accuse Y → X attack Y ', due to a mismatch between a verbal attack in r and an intended physical attack in h. Finally, r: 'X produce Y → X lay Y ' (applicable when X refers to poultry and Y to eggs) should not be matched in t: "Bugatti produce the fastest cars", due to a mismatch between the meanings of 'produce' in r and t. Overall, such incorrect inferences may be avoided by considering contextual information for t, h and r during their matching process.


That work, like ours, relies on pattern clusters.

in an evaluation based on an automatically created analogy SAT test we showed on two languages that pairs produced by our clusters indeed strongly reflect human notions of relation similarity.


It is also possible to restrict selection of hook words to a specific domain or word type, if we want to discover only a desired subset of existing relationships.

add all patterns of C 2 to C 1 , setting all shared patterns as core and all others as unconfirmed.


Remove all patterns originating from a single hook corpus.

we could sample hook words from nouns, verbs, proper names, or names of chemical compounds if we are only interested in discovering relationships between these.


The method of finding follow-up questions (Yang et al., 2006) from TREC context track could be adapted for context detection.


For Linear CRFs, dynamic programming is used to compute the maximum a posteriori (MAP) of y given x.

sVM, can be employed, where each pair of question and candidate context will be treated as an instance.


In addition to answer ranking, a complete system would have to search for similar questions already answered (Jeon et al., 2005), and rank content quality using "social" features such as the authority of users (Jeon et al., 2006;Agichtein et al., 2008).

our approach allows the learning of full transformations from question structures to answer structures using translation models applied to different text representations.


The performance of a tagger with such a policy is actually poor: there are dozens of tags in the tagset (3,561 in the case of Hebrew full morphological disambiguation) and only a few of them may match a given token.

we are currently investigating models that will take into account the specific nature of prefixes in Hebrew (which encode conjunctions, definite articles and prepositions) to better predict the segmentation of unknown words.


For each possible such segmentation, the full feature vector is constructed, and submitted to the Maximum Entropy model.

of this observation, our strategy is to focus on full morphological analysis for unknown tokens and apply a proper name classifier for unknown analyses and unknown tokens.


We utilize variable blocking by jointly sampling multiple segmentation and alignment decisions.

in this paper we investigate two questions: (i) Can we exploit cross-lingual correspondences to improve unsupervised language learning?


Therefore, we eliminated all parallel phrases that occur fewer than five times.

we notice that in general, adding Englishwhich has comparatively little morphological ambiguity -is about as useful as adding a more closely related Semitic language.


This measure is the harmonic mean of recall and precision, which are calculated on the basis of all possible segmentation points.

it must choose how many unaligned stray morphemes from language E, unaligned stray morphemes from language F, and abstract morphemes are to compose the parallel phrases.


When modeled in tandem, gains are observed for all language pairs, reducing relative error by as much as 24%.

once characterto-character phonetic correspondences are added as an abstract morpheme prior (final two rows), we find the performance of related language pairs outstrips English, reducing relative error over MONO-LINGUAL by 10% and 24% for the Hebrew/Arabic pair.


This step could probably be done automatically, but we consider it to be a non-issue in any realistic setting.

instead of inventing a new learning framework, we go back to the traditional EM trained HMMs.


(1995) developed a context-free method for acquiring morpho-lexical probabilities (p(t|w)) from an untagged corpus.

we presented a general family of algorithms to compute effective initial conditions: estimation of p(t|w) relying on an iterative process shifting probabilities between words and their contexts.


Unsupervised POS tagging has been traditionally approached with relative success (Merialdo, 1994;Kupiec, 1992) by HMM-based generative models, employing EM parameters estimation using the Baum-Welch algorithm.

, in order to support the wider tagset used by the KC analyzer: (1) The SW set for adjectives, copulas, existentials, personal pronouns, verbs and participles, is composed of all gender-number inflections; (2) The SW set for common nouns is composed of all number inflections, with definite article variation for absolute noun; (3) Prefix variations for proper nouns; (4) Gender variation for numerals; and (5) Gendernumber variation for all suffixes (possessive, nominative and accusative).


We use the same 24K word test-set as used in SE, TJ and GG, as well as the same diluted dictionaries.

better initial conditions bring us much over this particular local maximum -with an error reduction of 20%.


For each cluster we list all words occurring more than 1,000 times in the corpus.

the worst case complexity using the modified optimization criterion is in: Using this optimization criterion has two effects on the complexity of the algorithm.


Similar rules are applied for covering a wide range of node sequence patterns.

the decoding process is given an alternative path towards a translation table with less or no factors.


Note that MCCA can learn regular orthographic correspondences between source and target words, which is something edit distance cannot capture (see table 5).

we also explored how system performance varies for language pairs other than English-Spanish.


As described in Section 3.2, we constrain that the pattern words of an English pattern e must be extracted from a partial subtree.

in addition, the coverage of the extracted paraphrase patterns is high, which is above 84%.


Of the 740 cloze tests, 714 of the removed events were present in their respective list of guesses.

the discrete lists of events for which Schank scripts are most famous have not yet been constructed.


Hence, rjlu and AlTwylu agree in their Case ending 5 Without the explicit marking of the Case information, namely in the word endings, it could be equally valid that 'the-tall' modifies 'the-house' since they agree in Number, Gender and Definiteness as explicitly marked by the Definiteness article Al.

to improve the polynomial kernel, we sum it to the contribution of AST and/or EAST, obtaining AST+Poly3 (polynomial kernel of degree 3), EAST+Poly3 and AST+EAST+Poly3, whose F 1 scores are also shown in Figure 5.


In fact, when we use only the SVM regression model to rank the hypothesis sentences, without employing any classifier, then remove redundant sentences, rewrite and trim the results, we find that, interestingly, this approach also outperforms top-DUC2004, although the difference is not statistically significant.

to evaluate our biography generation system, we use the document sets created for the biography evalua-  Figure 1: Comparing our approaches against the top performing system in DUC2004 according to ROUGE-L (diamond).


An alternative way is to simply interpolate p(w|d) and p(w|C) with a constant coefficient: We will compare the two strategies in Section 5.

we also proposed new language models to capture the impact.


We find that the retrieval performance is generally sensitive to the smoothing parameters, suggesting that an understanding and appropriate setting of smoothing parameters is very important in the language modeling approach.

from Table 1, we see clearly that our method consistently outperforms all the baselines.


We define pr(s) as pr(s) = 1 α k , where k is the distance (counted in terms of the number of sentences) between sentence s and the center sentence of the window containing s; by "center sentence", we mean the citing sentence containing the citation label.

how to develop a better test collection is an important future direction.


Technical paper summarization has also been studied (Paice, 1981;Paice and Jones, 1993;Saggion and Lapalme, 2002;Teufel and Moens, 2002), but the previous work did not explore citation context to emphasize the impact of papers.

citation contexts serve as an indicator of the impact of the paper, but the summary is generated by extracting the sentences from the original paper.


Citation context has been explored in several studies (Nakov et al., 2004;Ritchie et al., 2006;Schwartz et al., 2007;Siddharthan and Teufel, 2007).

unlike in the case of ad hoc retrieval, we do not really have a query describing the impact of the paper; instead, we have a lot of citation contexts that can be used to infer information about the query.


For this reason, understanding what aspects of the input make it difficult for summarization becomes an interesting and important issue that has not been addressed in the summarization community untill now.

the data used for the analyses comes from the annual Document Understanding Conference (DUC) in which various summarization approaches are evaluated on common data, with new test sets provided each year.


(2005) attempt to deal with this problem by using a fuzzy algorithm to cluster speakers; this assigns each speaker a distribution over conversations rather than a hard assignment.

our annotation scheme requires annotators to mark each utterance as part of a single conversation, and distinct conversations are not related in any way.


The disentanglement system's test performance decreases proportionally; mean 1-to-1 falls to 36.08, and mean loc 3 to 63.00, essentially baseline performance.

if we run our classifier on a pair of very distant utterances, we expect it to default to the majority class, which in this case will be different, and this will damage our performance in case the two are really part of the same conversation.


The better performance against the EM model with heuristically designed features also suggests that ILP is able to learn effective first-order rules for the coreference resolution task.

mcCallum and Wellner (2003) propose several graphical models for coreference analysis.


And our experimental results on the ACE data set shows the model is effective for coreference resolution.

the lexical and grammatical features are computed by testing any mention 1 in the entity against the active mention, for ex-1 Linguistically, pronouns usually have the most direct coref-ample, the feature nameAlias is assigned value 1 if at least one mention in the entity is a name alias of the active mention.


The symmetric Dirichlet prior α acts as a smoothing pseudo-count.

sampling or gradient-based techniques may be used to estimate these parameters, but this is left for future work.


Such an approach would be incapable of flexibly balancing the contributions of each modality.

we focus on improving discourse processing on unconstrained natural language between humans.